160 citations · 618 across the 56 of their papers we have counts for
60 papers
Power-MSE trade-off of Factorized Low-rank Approximated Computation Scheme with Memristors
Binyu Lu, Matthias Frey, Stark Draper +1
Memristor crossbars enable analog vector-matrix multiplication (VMM) which is promising for machine learning applications. Scaling matrix entries to lower memristor conductance lev…
Modulo Quantization Coding for Primitive Relay and Diamond Channels with Correlated Noises
Yuanxin Guo, Stark C. Draper, Wei Yu
This paper proposes modulo quantization (MQ) coding as a simple, structured, and low-complexity scheme for channels with primitive (i.e., noiseless digital) relay links and correla…
AstroSURE: Learning to Remove Noise from Astronomical Images Without Ground Truth Data
Omid Vaheb, Sebastien Fabbro, Stark Draper
In astronomical imaging, the low photon count of exposures necessitates extensive post-processing steps, including contamination removal and denoising. This paper evaluates deep-le…
Low-Rank-Based Approximate Computation with Memristors
Binyu Lu, Matthias Frey, Stark Draper +1
Memristor crossbars enable vector-matrix multiplication (VMM), and are promising for low-power applications. However, it can be difficult to write the memristor conductance values…
Controlled privacy leakage propagation throughout overlapping grouped learning
Shahrzad Kiani, Franziska Boenisch, Stark C. Draper
Federated Learning (FL) is the standard protocol for collaborative learning. In FL, multiple workers jointly train a shared model. They exchange model updates calculated on their d…
Differentially Private Federated Learning With Time-Adaptive Privacy Spending
Shahrzad Kiani, Nupur Kulkarni, Adam Dziedzic +2
Federated learning (FL) with differential privacy (DP) provides a framework for collaborative machine learning, enabling clients to train a shared model while adhering to strict pr…